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Abstract Children with feeding difficulties often engage in expulsion (i.e., spitting out) of liquid. Expulsion is problematic because it limits the volume of liquid that a child will consume. Researchers have used re‐presentation as an embedded component of escape extinction to treat expel. Although studies have demonstrated that re‐presentation can effectively reduce expel, it is unclear whether it should always be included with escape‐extinction‐based treatments. We describe a program evaluation project designed to examine the effects of re‐presentation on liquid expulsion for children with severe feeding difficulties. We conducted a prospective consecutive controlled case series to compare the effects of a function‐based treatment with and without re‐presentation and reported on the outcomes obtained for 17 children. Various patterns of responding emerged across participants. However, re‐presentation resulted in the greatest increases in mouth clean (i.e., swallowing) and lowest levels of expel for 10 participants. We discuss implications for research and practice.

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ABSTRACT There is increased demand for supervisors to develop staff training procedures that are efficient, such as those that promote generalized responding. Computer‐based instruction (CBI) is effective and efficient and can be enhanced by general‐case procedures. Therefore, experiment one employed a non‐concurrent multiple baseline design with adults to evaluate the generalized effects of general‐case analysis to inform CBI. We found this combination moderately effective in facilitating generalization of staff training. Experiment two used a non‐concurrent multiple baseline to evaluate general case analysis and modified general case programming to inform CBI to specifically increase recombinative generalization. We then identified the extent of recombinative generalization along a recombinative generalization gradient. Most participants demonstrated recombinative generalization and that fewer combinations of procedures were more likely to occur than all of them combined. Implications for staff trainers and researchers include pre‐training of skill acquisition trial arrangements and further evaluations of general‐case procedures.

Abstract The characterization of Pavlovian conditioning in behavior analysis is outdated. Pavlovian conditioning is not limited to the conditioning of glandular and visceral responses; it also involves responses that participate in operant contingencies, and the elicitation of motivational or emotional states that affect operant behavior. Contemporary formulations emphasize the inseparability of behavior from the antecedent contextual stimuli in the presence of which the behavior occurs. Operant procedures also involve various relations between behavior and antecedent and consequential stimuli, making Pavlovian relations an integral but often overlooked part of operant procedures. Considering these issues encourages increased attention to the relationship of behavior to antecedent context and interactions between Pavlovian and operant relations in applied behavior analysis procedures. We argue that adopting a modern view of Pavlovian conditioning should increase the analytic tools available to behavior analysts, and to the development of new and more effective treatment strategies in the domains of maintenance/generalization of behavior change and behavioral assessment.

Abstract As contingency management (CM) moves from research to practice, researchers have a responsibility to outline the minimum procedural necessities that lead to an effective, sustainable treatment that can be implemented as a mainstream therapy for substance use disorders. To begin identifying the minimum requirements, the purpose of the current study was to provide framework and a first step toward building a risk calculator that predicts treatment outcomes in CM, and can predict the optimal incentive size to prescribe by evaluating behavioral economic factors, demographic variables, and use severity measures in individuals who completed CM treatment for alcohol use. Participants were 38 individuals enrolled in the active treatment arms of two parent CM studies for reducing alcohol use (Koffarnus et al., 2018; Koffarnus et al., 2021). Participants were 42 years old on average, 55% male, and a majority were white, non-Hispanic. Fifteen candidate predictor variables were assessed for inclusion in the predictive model including demographic variables, use severity scores, and behavioral economic parameters. A logistic regression framework was used to identify top predictive models. Accuracy was assessed by computing receiver operating characteristic (ROC) curves and area under the curves. A model including the delay discounting parameter, log 10 (ED50) of alcohol, participant age, and Beck’s Anxiety Inventory score was predictive of treatment outcomes in the current sample. The results demonstrate the utility of the ROC analysis as a method for identifying a predictive model. Further research is needed to replicate and verify the findings of the current analysis.

ABSTRACT Function‐based interventions are commonly employed by behavioral practitioners to address challenging behavior sometimes displayed by individuals with autism spectrum disorder (ASD). To select a function‐based intervention for implementation, practitioners first need conduct a functional behavior assessment (FBA). Given that individuals with ASD spend much of their time in school settings, it may be worthwhile to train more individuals to participate in portions of the FBA process. The purpose of this study was to evaluate the effectiveness of using a decision‐making model, a tool used to systematically guide practitioners, to train preschool staff to collect descriptive assessment data. Six staff members working at a preschool for children with ASD participated in this study. We found that for four of six staff members, the decision‐making model was effective in producing increases in accurate data collection. Two participants required one session of performance feedback, using the decision‐making model, to increase their accuracy in data collection.

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